Estimation of joint torque in dynamic activities using wearable A-mode ultrasound

Yichu Jin, Jonathan T. Alvarez, Elizabeth L. Suitor, Krithika Swaminathan, Andrew Chin, Umut S. Civici, Richard W. Nuckols, Robert D. Howe and Conor J. Walsh ()
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Yichu Jin: Harvard University
Jonathan T. Alvarez: Harvard University
Elizabeth L. Suitor: Harvard University
Krithika Swaminathan: Harvard University
Andrew Chin: Harvard University
Umut S. Civici: Harvard University
Richard W. Nuckols: Harvard University
Robert D. Howe: Harvard University
Conor J. Walsh: Harvard University

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract The human body constantly experiences mechanical loading. However, quantifying internal loads within the musculoskeletal system remains challenging, especially during unconstrained dynamic activities. Conventional measures are constrained to laboratory settings, and existing wearable approaches lack muscle specificity or validation during dynamic movement. Here, we present a strategy for estimating corresponding joint torque from muscles with different architectures during various dynamic activities using wearable A-mode ultrasound. We first introduce a method to track changes in muscle thickness using single-element ultrasonic transducers. We then estimate elbow and knee torque with errors less than 7.6% and coefficients of determination (R2) greater than 0.92 during controlled isokinetic contractions. Finally, we demonstrate wearable joint torque estimation during dynamic real-world tasks, including weightlifting, cycling, and both treadmill and outdoor locomotion. The capability to assess joint torque during unconstrained real-world activities can provide new insights into muscle function and movement biomechanics, with potential applications in injury prevention and rehabilitation.

Date: 2024
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DOI: 10.1038/s41467-024-50038-0

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